In this article, a three-objective optimization technique constructed on Non-dominated Sorting Genetic Algorithm (NSGA-II) was used to increase the coefficient of performance (COP) and exergy efficiency () of a two-stage vapor compression cascade refrigeration system (CCRS) and decrease its total cost rate ( ). The model was built in MATLAB and the refrigerants thermo-physical properties were probed by using REFPROP. Since refrigerant mixtures with zeotropic behavior can improve the energy efficiency of the system, the binary refrigerant mixtures with variable mass fractions were used on both sides of CCRS. The cycle optimization was done based on 10 design variables. The first six design variables were refrigerant type and their mass fractions on the low temperature cycle (LTC) and high temperature cycle (HTC). The saturated vapor temperature in the condenser and cascade heat exchanger (CHX) inlets, the evaporator inlet temperature, and the two-phase temperature in the CHX inlet were selected as the remaining four design variables. The cooling capacity and evaporator and condenser secondary flow conditions were kept fixed during the optimization process. The comparison of optimization results with the base case showed a 15.55% and 17.74% increase in COP and , respectively. while the total cost rate ( ) showed a 6 .07% reduction. Finally, the optimization process led to a 24.47% and 13.1% reduction in the CCRS cycle total exergy destruction and maximum cycle temperature.

Cascade refrigeration, Coefficient of performance, Exergy efficiency, Cost analysis, Three-objective optimization, Mixed refrigerant

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